from sklearn_benchmarks.reporting.hp_match import HpMatchReporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

scikit-learn vs. scikit-learn-intelex (Intel® oneAPI)

measurements = ['iteration_throughput', 'latency', 'mean_duration_sklearn', 'mean_duration_sklearnex', 'speedup', 'std_duration_sklearn', 'std_duration_sklearnex', 'std_speedup']

def get_position(string):
    if "mean_duration" in string:
        return 3
    elif "std_duration" in string:
        return 2
    elif "score" in string:
        return 1
    elif "speedup" in string:
        return 0
    else:
        return -1

sorted(measurements, key=get_position, reverse=True) 

['mean_duration_sklearn',
 'mean_duration_sklearnex',
 'std_duration_sklearn',
 'std_duration_sklearnex',
 'speedup',
 'std_speedup',
 'iteration_throughput',
 'latency']

reporting = HpMatchReporting(against_lib="sklearnex", config="config.yml", log_scale=True)
reporting.make_report()

We assume here there is a perfect match between the hyperparameters of both librairies. For a given set of parameters and a given dataset, we compute the speedup time scikit-learn / time sklearnex. For instance, a speedup of 2 means that sklearnex is twice as fast as scikit-learn for a given set of parameters and a given dataset.

KNeighborsClassifier_brute_force

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=brute.

WARNING! Mismatch between validation scores for 14 prediction (58.33%). The observed differences can be found in the diff_accuracy_scores column. The chosen difference threshold is 0.001 for accuracy_score. See details in the dataframe below.

	estimator	function	n_samples_train	n_samples	n_features	parameters_digest	dataset_digest	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup	diff_accuracy_scores
1	KNeighborsClassifier_brute_force	predict	100000	1000	100	e70d8e7886766d41f30889506baa1e67	2cf49829391aa7891e877f2ed070adf0	2.027753	0.260970	NaN	0.000395	0.002028	brute	-1	1	0.663	0.401495	0.008845	0.687	5.050505	5.051730	0.024
4	KNeighborsClassifier_brute_force	predict	100000	1000	100	22d5eac4a4146149b35eae6914921514	2cf49829391aa7891e877f2ed070adf0	2.776739	0.044499	NaN	0.000288	0.002777	brute	-1	5	0.757	0.392266	0.006000	0.742	7.078715	7.079543	0.015
7	KNeighborsClassifier_brute_force	predict	100000	1000	100	16f94fc93942ff7ece860c9b0f64645f	2cf49829391aa7891e877f2ed070adf0	2.158312	0.024728	NaN	0.000371	0.002158	brute	1	100	0.882	0.458089	0.008246	0.875	4.711559	4.712322	0.007
8	KNeighborsClassifier_brute_force	predict	100000	1	100	16f94fc93942ff7ece860c9b0f64645f	2cf49829391aa7891e877f2ed070adf0	0.023255	0.000925	NaN	0.000034	0.023255	brute	1	100	1.000	0.011583	0.001696	0.000	2.007716	2.029136	1.000
10	KNeighborsClassifier_brute_force	predict	100000	1000	100	2d5f2b2c02d77766434d9e5033b4a76c	2cf49829391aa7891e877f2ed070adf0	2.758314	0.045003	NaN	0.000290	0.002758	brute	-1	100	0.882	0.444168	0.005463	0.875	6.210074	6.210544	0.007
11	KNeighborsClassifier_brute_force	predict	100000	1	100	2d5f2b2c02d77766434d9e5033b4a76c	2cf49829391aa7891e877f2ed070adf0	0.024451	0.001874	NaN	0.000033	0.024451	brute	-1	100	1.000	0.010426	0.000624	0.000	2.345216	2.349416	1.000
13	KNeighborsClassifier_brute_force	predict	100000	1000	100	efac583623711d395ddae7a1e881ff68	2cf49829391aa7891e877f2ed070adf0	2.131869	0.024922	NaN	0.000375	0.002132	brute	1	5	0.757	0.398827	0.006950	0.742	5.345351	5.346162	0.015
16	KNeighborsClassifier_brute_force	predict	100000	1000	100	b79d63bb7670c492de5c3befac58fe29	2cf49829391aa7891e877f2ed070adf0	1.345079	0.016867	NaN	0.000595	0.001345	brute	1	1	0.663	0.389310	0.006047	0.687	3.455032	3.455449	0.024
19	KNeighborsClassifier_brute_force	predict	100000	1000	2	e70d8e7886766d41f30889506baa1e67	88843f54689e3271092f70126e1de585	1.637761	0.036030	NaN	0.000010	0.001638	brute	-1	1	0.896	0.083410	0.002690	0.967	19.634964	19.645175	0.071
22	KNeighborsClassifier_brute_force	predict	100000	1000	2	22d5eac4a4146149b35eae6914921514	88843f54689e3271092f70126e1de585	2.464218	0.055673	NaN	0.000006	0.002464	brute	-1	5	0.922	0.085323	0.003310	0.974	28.881189	28.902909	0.052
25	KNeighborsClassifier_brute_force	predict	100000	1000	2	16f94fc93942ff7ece860c9b0f64645f	88843f54689e3271092f70126e1de585	1.907462	0.016050	NaN	0.000008	0.001907	brute	1	100	0.929	0.135446	0.004620	0.975	14.082832	14.091020	0.046
28	KNeighborsClassifier_brute_force	predict	100000	1000	2	2d5f2b2c02d77766434d9e5033b4a76c	88843f54689e3271092f70126e1de585	2.472077	0.029250	NaN	0.000006	0.002472	brute	-1	100	0.929	0.133449	0.004922	0.975	18.524440	18.537036	0.046
31	KNeighborsClassifier_brute_force	predict	100000	1000	2	efac583623711d395ddae7a1e881ff68	88843f54689e3271092f70126e1de585	1.918974	0.019640	NaN	0.000008	0.001919	brute	1	5	0.922	0.083788	0.001841	0.974	22.902687	22.908213	0.052
34	KNeighborsClassifier_brute_force	predict	100000	1000	2	b79d63bb7670c492de5c3befac58fe29	88843f54689e3271092f70126e1de585	1.105213	0.014106	NaN	0.000014	0.001105	brute	1	1	0.896	0.083515	0.003586	0.967	13.233668	13.245863	0.071

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.001	6.665	0.0	-1	1	0.050	0.006	0.241	0.242	See	See
3	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.000	6.621	0.0	-1	5	0.051	0.003	0.236	0.236	See	See
6	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.001	6.541	0.0	1	100	0.051	0.002	0.242	0.242	See	See
9	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.013	0.001	6.265	0.0	-1	100	0.051	0.002	0.251	0.251	See	See
12	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.000	6.777	0.0	1	5	0.048	0.001	0.245	0.245	See	See
15	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.013	0.001	6.194	0.0	1	1	0.049	0.001	0.263	0.263	See	See
18	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	0.350	0.0	-1	1	0.009	0.000	0.506	0.507	See	See
21	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	0.348	0.0	-1	5	0.009	0.001	0.523	0.526	See	See
24	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	0.348	0.0	1	100	0.009	0.000	0.525	0.526	See	See
27	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	0.344	0.0	-1	100	0.009	0.001	0.533	0.541	See	See
30	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.001	0.352	0.0	1	5	0.008	0.000	0.540	0.540	See	See
33	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.000	0.356	0.0	1	1	0.009	0.000	0.516	0.517	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.028	0.261	0.000	0.002	-1	1	0.401	0.009	5.051	5.052	See	See
2	KNeighborsClassifier_brute_force	predict	100000	1	100	0.026	0.002	0.000	0.026	-1	1	0.011	0.001	2.448	2.452	See	See
4	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.777	0.044	0.000	0.003	-1	5	0.392	0.006	7.079	7.080	See	See
5	KNeighborsClassifier_brute_force	predict	100000	1	100	0.025	0.002	0.000	0.025	-1	5	0.010	0.000	2.382	2.383	See	See
7	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.158	0.025	0.000	0.002	1	100	0.458	0.008	4.712	4.712	See	See
8	KNeighborsClassifier_brute_force	predict	100000	1	100	0.023	0.001	0.000	0.023	1	100	0.012	0.002	2.008	2.029	See	See
10	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.758	0.045	0.000	0.003	-1	100	0.444	0.005	6.210	6.211	See	See
11	KNeighborsClassifier_brute_force	predict	100000	1	100	0.024	0.002	0.000	0.024	-1	100	0.010	0.001	2.345	2.349	See	See
13	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.132	0.025	0.000	0.002	1	5	0.399	0.007	5.345	5.346	See	See
14	KNeighborsClassifier_brute_force	predict	100000	1	100	0.023	0.001	0.000	0.023	1	5	0.011	0.001	2.026	2.034	See	See
16	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.345	0.017	0.001	0.001	1	1	0.389	0.006	3.455	3.455	See	See
17	KNeighborsClassifier_brute_force	predict	100000	1	100	0.021	0.001	0.000	0.021	1	1	0.011	0.001	1.933	1.937	See	See
19	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.638	0.036	0.000	0.002	-1	1	0.083	0.003	19.635	19.645	See	See
20	KNeighborsClassifier_brute_force	predict	100000	1	2	0.005	0.003	0.000	0.005	-1	1	0.001	0.000	9.485	9.535	See	See
22	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.464	0.056	0.000	0.002	-1	5	0.085	0.003	28.881	28.903	See	See
23	KNeighborsClassifier_brute_force	predict	100000	1	2	0.007	0.002	0.000	0.007	-1	5	0.001	0.000	11.436	11.505	See	See
25	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.907	0.016	0.000	0.002	1	100	0.135	0.005	14.083	14.091	See	See
26	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	0.000	0.003	1	100	0.001	0.000	4.129	4.297	See	See
28	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.472	0.029	0.000	0.002	-1	100	0.133	0.005	18.524	18.537	See	See
29	KNeighborsClassifier_brute_force	predict	100000	1	2	0.009	0.002	0.000	0.009	-1	100	0.001	0.000	13.829	13.939	See	See
31	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.919	0.020	0.000	0.002	1	5	0.084	0.002	22.903	22.908	See	See
32	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	0.000	0.003	1	5	0.001	0.000	5.088	5.131	See	See
34	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.105	0.014	0.000	0.001	1	1	0.084	0.004	13.234	13.246	See	See
35	KNeighborsClassifier_brute_force	predict	100000	1	2	0.002	0.000	0.000	0.002	1	1	0.001	0.000	3.045	3.137	See	See

KNeighborsClassifier_kd_tree

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=kd_tree.

WARNING! Mismatch between validation scores for 12 prediction (50.0%). The observed differences can be found in the diff_accuracy_scores column. The chosen difference threshold is 0.001 for accuracy_score. See details in the dataframe below.

	estimator	function	n_samples_train	n_samples	n_features	parameters_digest	dataset_digest	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup	diff_accuracy_scores
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	fe8267e25dadb302ed498e50fabf7ef4	1eb6cf1a720a225efb91df0b529b0510	0.882532	1.124641	NaN	0.000091	0.000883	kd_tree	-1	1	0.929	0.125178	0.004629	0.910	7.050240	7.055060	0.019
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	dc74b969bf622bc24ba3bc62c980983b	1eb6cf1a720a225efb91df0b529b0510	1.098293	0.383205	NaN	0.000073	0.001098	kd_tree	-1	5	0.946	0.221112	0.003932	0.941	4.967132	4.967917	0.005
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	41accfcdcd5c50784bedf6164b63de99	1eb6cf1a720a225efb91df0b529b0510	5.737746	0.794953	NaN	0.000014	0.005738	kd_tree	1	100	0.951	0.684492	0.015340	0.940	8.382492	8.384597	0.011
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	022f7445d43bb1dbc24dc3106c03cb93	1eb6cf1a720a225efb91df0b529b0510	3.216986	0.250691	NaN	0.000025	0.003217	kd_tree	-1	100	0.951	0.672461	0.005641	0.940	4.783899	4.784067	0.011
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	84622ba45e941db642965553529e1941	1eb6cf1a720a225efb91df0b529b0510	1.659354	0.213759	NaN	0.000048	0.001659	kd_tree	1	5	0.946	0.228686	0.005619	0.941	7.256027	7.258217	0.005
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	a9455565db1d8e052a783317c99744ff	1eb6cf1a720a225efb91df0b529b0510	0.936813	0.277644	NaN	0.000085	0.000937	kd_tree	1	1	0.929	0.125561	0.003632	0.910	7.461034	7.464154	0.019
19	KNeighborsClassifier_kd_tree	predict	1000	1000	2	fe8267e25dadb302ed498e50fabf7ef4	edf7cb014c2c0910e1c3aaaf8ae633f2	0.035003	0.014955	NaN	0.000457	0.000035	kd_tree	-1	1	0.891	0.000486	0.000024	0.879	72.042404	72.132716	0.012
22	KNeighborsClassifier_kd_tree	predict	1000	1000	2	dc74b969bf622bc24ba3bc62c980983b	edf7cb014c2c0910e1c3aaaf8ae633f2	0.031504	0.004727	NaN	0.000508	0.000032	kd_tree	-1	5	0.911	0.000826	0.000059	0.905	38.129740	38.227709	0.006
25	KNeighborsClassifier_kd_tree	predict	1000	1000	2	41accfcdcd5c50784bedf6164b63de99	edf7cb014c2c0910e1c3aaaf8ae633f2	0.043169	0.011873	NaN	0.000371	0.000043	kd_tree	1	100	0.894	0.005372	0.000384	0.917	8.036307	8.056851	0.023
28	KNeighborsClassifier_kd_tree	predict	1000	1000	2	022f7445d43bb1dbc24dc3106c03cb93	edf7cb014c2c0910e1c3aaaf8ae633f2	0.047777	0.007131	NaN	0.000335	0.000048	kd_tree	-1	100	0.894	0.007168	0.002199	0.917	6.664937	6.971576	0.023
31	KNeighborsClassifier_kd_tree	predict	1000	1000	2	84622ba45e941db642965553529e1941	edf7cb014c2c0910e1c3aaaf8ae633f2	0.027488	0.001631	NaN	0.000582	0.000027	kd_tree	1	5	0.911	0.000826	0.000125	0.905	33.286679	33.668114	0.006
34	KNeighborsClassifier_kd_tree	predict	1000	1000	2	a9455565db1d8e052a783317c99744ff	edf7cb014c2c0910e1c3aaaf8ae633f2	0.025897	0.001193	NaN	0.000618	0.000026	kd_tree	1	1	0.891	0.000510	0.000014	0.879	50.813088	50.833015	0.012

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	2.844	0.046	0.028	0.0	-1	1	0.779	0.047	3.651	3.657	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	4.413	0.068	0.018	0.0	-1	5	0.746	0.012	5.914	5.915	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	4.395	0.042	0.018	0.0	1	100	0.731	0.045	6.013	6.024	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	4.387	0.056	0.018	0.0	-1	100	0.759	0.012	5.778	5.779	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	4.472	0.085	0.018	0.0	1	5	0.720	0.017	6.210	6.212	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	4.419	0.056	0.018	0.0	1	1	0.749	0.008	5.899	5.899	See	See
18	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.001	0.021	0.0	-1	1	0.004	0.003	0.184	0.238	See	See
21	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.001	0.015	0.0	-1	5	0.002	0.002	0.636	0.977	See	See
24	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.023	0.0	1	100	0.001	0.001	0.540	0.661	See	See
27	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.023	0.0	-1	100	0.001	0.000	0.657	0.673	See	See
30	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.023	0.0	1	5	0.001	0.000	0.734	0.736	See	See
33	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.027	0.0	1	1	0.001	0.000	0.623	0.628	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.883	1.125	0.000	0.001	-1	1	0.125	0.005	7.050	7.055	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	0.000	0.003	-1	1	0.000	0.000	12.042	12.297	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.098	0.383	0.000	0.001	-1	5	0.221	0.004	4.967	4.968	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.004	0.002	0.000	0.004	-1	5	0.001	0.000	7.483	8.147	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	5.738	0.795	0.000	0.006	1	100	0.684	0.015	8.382	8.385	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	0.000	0.003	1	100	0.001	0.000	3.304	3.576	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	3.217	0.251	0.000	0.003	-1	100	0.672	0.006	4.784	4.784	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.006	0.001	0.000	0.006	-1	100	0.001	0.000	7.552	7.740	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.659	0.214	0.000	0.002	1	5	0.229	0.006	7.256	7.258	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.002	0.000	0.000	0.002	1	5	0.000	0.000	3.675	3.830	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.937	0.278	0.000	0.001	1	1	0.126	0.004	7.461	7.464	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.001	0.000	0.000	0.001	1	1	0.000	0.000	3.979	4.114	See	See
19	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.035	0.015	0.000	0.000	-1	1	0.000	0.000	72.042	72.133	See	See
20	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.003	0.000	0.000	0.003	-1	1	0.000	0.000	23.387	23.615	See	See
22	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.032	0.005	0.001	0.000	-1	5	0.001	0.000	38.130	38.228	See	See
23	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	0.000	0.002	-1	5	0.000	0.000	18.156	18.371	See	See
25	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.043	0.012	0.000	0.000	1	100	0.005	0.000	8.036	8.057	See	See
26	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	100	0.000	0.000	4.641	4.800	See	See
28	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.048	0.007	0.000	0.000	-1	100	0.007	0.002	6.665	6.972	See	See
29	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	0.000	0.002	-1	100	0.000	0.000	16.437	16.549	See	See
31	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.027	0.002	0.001	0.000	1	5	0.001	0.000	33.287	33.668	See	See
32	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	5	0.000	0.000	5.352	5.390	See	See
34	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.026	0.001	0.001	0.000	1	1	0.001	0.000	50.813	50.833	See	See
35	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	1	0.000	0.000	5.728	5.767	See	See

KMeans_tall

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_tall	fit	1000000	1000000	2	0.560	0.080	30	0.029	0.0	random	0.281	0.008	1.993	1.994	See	See
3	KMeans_tall	fit	1000000	1000000	2	0.622	0.024	30	0.026	0.0	k-means++	0.314	0.007	1.979	1.980	See	See
6	KMeans_tall	fit	1000000	1000000	100	6.554	0.124	30	0.122	0.0	random	3.742	0.052	1.751	1.752	See	See
9	KMeans_tall	fit	1000000	1000000	100	6.890	0.060	30	0.116	0.0	k-means++	3.914	0.027	1.760	1.760	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_tall	predict	1000000	1000	2	0.002	0.001	30	0.008	0.000	random	0.0	0.0	7.769	10.130	See	See
2	KMeans_tall	predict	1000000	1	2	0.002	0.000	30	0.000	0.002	random	0.0	0.0	10.788	10.827	See	See
4	KMeans_tall	predict	1000000	1000	2	0.002	0.001	30	0.008	0.000	k-means++	0.0	0.0	10.770	11.079	See	See
5	KMeans_tall	predict	1000000	1	2	0.002	0.000	30	0.000	0.002	k-means++	0.0	0.0	12.224	12.281	See	See
7	KMeans_tall	predict	1000000	1000	100	0.002	0.000	30	0.409	0.000	random	0.0	0.0	6.909	7.060	See	See
8	KMeans_tall	predict	1000000	1	100	0.002	0.000	30	0.001	0.002	random	0.0	0.0	9.231	9.300	See	See
10	KMeans_tall	predict	1000000	1000	100	0.002	0.000	30	0.407	0.000	k-means++	0.0	0.0	7.503	7.704	See	See
11	KMeans_tall	predict	1000000	1	100	0.002	0.000	30	0.000	0.002	k-means++	0.0	0.0	10.183	10.447	See	See

KMeans_short

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

WARNING! Mismatch between validation scores for 4 prediction (50.0%). The observed differences can be found in the diff_adjusted_rand_scores column. The chosen difference threshold is 0.001 for adjusted_rand_score. See details in the dataframe below.

	estimator	function	n_samples_train	n_samples	n_features	parameters_digest	dataset_digest	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	init	max_iter	n_clusters	n_init	tol	adjusted_rand_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	adjusted_rand_score_sklearnex	speedup	std_speedup	diff_adjusted_rand_scores
1	KMeans_short	predict	10000	1000	2	3db9c030f488d0e8ff350ae449da4627	058b7e3842b587a5c675518f0706f5ee	0.002129	0.000176	20	0.007515	0.000002	full	random	20	300	1	1.000000e-16	0.000126	0.000568	0.000093	-0.000965	3.748420	3.798222	0.001090
4	KMeans_short	predict	10000	1000	2	ac9b8cec4749455c0d1cd1a92c150716	058b7e3842b587a5c675518f0706f5ee	0.002147	0.000404	20	0.007451	0.000002	full	k-means++	20	300	1	1.000000e-16	0.001245	0.000648	0.000232	-0.000750	3.316129	3.522789	0.001995
7	KMeans_short	predict	10000	1000	100	3db9c030f488d0e8ff350ae449da4627	7f85b913f395ec54101a6738ea63a9a7	0.003165	0.000222	20	0.252792	0.000003	full	random	20	300	1	1.000000e-16	0.278733	0.001392	0.000051	0.293767	2.273507	2.275012	0.015034
10	KMeans_short	predict	10000	1000	100	ac9b8cec4749455c0d1cd1a92c150716	7f85b913f395ec54101a6738ea63a9a7	0.002979	0.000218	20	0.268570	0.000003	full	k-means++	20	300	1	1.000000e-16	0.317011	0.001402	0.000089	0.256968	2.124967	2.129286	0.060044

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_short	fit	10000	10000	2	0.095	0.004	20	0.002	0.0	random	0.050	0.003	1.917	1.920	See	See
3	KMeans_short	fit	10000	10000	2	0.290	0.009	20	0.001	0.0	k-means++	0.124	0.003	2.344	2.345	See	See
6	KMeans_short	fit	10000	10000	100	0.319	0.011	20	0.025	0.0	random	0.251	0.006	1.275	1.275	See	See
9	KMeans_short	fit	10000	10000	100	1.061	0.017	20	0.008	0.0	k-means++	0.584	0.008	1.818	1.819	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_short	predict	10000	1000	2	0.002	0.0	20	0.008	0.000	random	0.001	0.0	3.748	3.798	See	See
2	KMeans_short	predict	10000	1	2	0.002	0.0	20	0.000	0.002	random	0.000	0.0	10.099	10.293	See	See
4	KMeans_short	predict	10000	1000	2	0.002	0.0	20	0.007	0.000	k-means++	0.001	0.0	3.316	3.523	See	See
5	KMeans_short	predict	10000	1	2	0.002	0.0	20	0.000	0.002	k-means++	0.000	0.0	10.868	10.981	See	See
7	KMeans_short	predict	10000	1000	100	0.003	0.0	20	0.253	0.000	random	0.001	0.0	2.274	2.275	See	See
8	KMeans_short	predict	10000	1	100	0.002	0.0	20	0.000	0.002	random	0.000	0.0	8.555	8.694	See	See
10	KMeans_short	predict	10000	1000	100	0.003	0.0	20	0.269	0.000	k-means++	0.001	0.0	2.125	2.129	See	See
11	KMeans_short	predict	10000	1	100	0.002	0.0	20	0.000	0.002	k-means++	0.000	0.0	8.847	8.911	See	See

LogisticRegression

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

WARNING! Mismatch between validation scores for 2 prediction (50.0%). The observed differences can be found in the diff_accuracy_scores column. The chosen difference threshold is 0.001 for accuracy_score. See details in the dataframe below.

	estimator	function	n_samples_train	n_samples	n_features	parameters_digest	dataset_digest	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	C	class_weight	dual	fit_intercept	intercept_scaling	l1_ratio	max_iter	multi_class	n_jobs	penalty	random_state	solver	tol	verbose	warm_start	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup	diff_accuracy_scores
1	LogisticRegression	predict	1000000	1000	100	f11be2529b0a77bc8fb5ee8de141e6b2	09c36ed6521e03974d0c134895f56c01	0.000506	0.000442	[20]	1.582343	5.055793e-07	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0001	0	False	0.56	0.000763	0.001259	0.55	0.662326	1.277652	0.01
4	LogisticRegression	predict	1000	100	10000	f11be2529b0a77bc8fb5ee8de141e6b2	7daecaea01e5baa8c862f264e3c1c3b0	0.002749	0.001288	[26]	2.910600	2.748574e-05	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0001	0	False	0.35	0.007384	0.001184	0.28	0.372229	0.376987	0.07

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	LogisticRegression	fit	1000000	1000000	100	15.662	0.355	[20]	0.051	0.000	2.802	0.063	5.589	5.591	See	See
3	LogisticRegression	fit	1000	1000	10000	1.416	0.730	[26]	0.057	0.001	1.097	0.015	1.291	1.291	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	LogisticRegression	predict	1000000	1000	100	0.001	0.000	[20]	1.582	0.0	0.001	0.001	0.662	1.278	See	See
2	LogisticRegression	predict	1000000	1	100	0.000	0.000	[20]	0.011	0.0	0.000	0.000	0.291	0.326	See	See
4	LogisticRegression	predict	1000	100	10000	0.003	0.001	[26]	2.911	0.0	0.007	0.001	0.372	0.377	See	See
5	LogisticRegression	predict	1000	1	10000	0.000	0.000	[26]	0.776	0.0	0.002	0.000	0.062	0.062	See	See

Ridge

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

WARNING! Mismatch between validation scores for 1 prediction (25.0%). The observed differences can be found in the diff_r2_scores column. The chosen difference threshold is 0.001 for r2_score. See details in the dataframe below.

	estimator	function	n_samples_train	n_samples	n_features	parameters_digest	dataset_digest	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	alpha	copy_X	fit_intercept	max_iter	normalize	random_state	solver	tol	r2_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	r2_score_sklearnex	speedup	std_speedup	diff_r2_scores
1	Ridge	predict	1000	1000	10000	a70ae8dc5c059ec88cf27c61016aee2a	14fae294d530397144c65ba3209bf125	0.011859	0.002041	NaN	6.745977	0.000012	1.0	True	True	NaN	deprecated	NaN	auto	0.001	0.082567	0.019529	0.002251	0.122191	0.607255	0.611276	0.039624

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	Ridge	fit	1000	1000	10000	0.287	0.006	0.279	0.0	0.293	0.006	0.978	0.979	See	See
3	Ridge	fit	1000000	1000000	100	1.250	0.078	0.640	0.0	0.416	0.242	3.005	3.476	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	Ridge	predict	1000	1000	10000	0.012	0.002	6.746	0.0	0.02	0.002	0.607	0.611	See	See
2	Ridge	predict	1000	1	10000	0.000	0.000	0.867	0.0	0.00	0.000	0.637	0.654	See	See
4	Ridge	predict	1000000	1000	100	0.000	0.000	5.180	0.0	0.00	0.000	0.410	0.677	See	See
5	Ridge	predict	1000000	1	100	0.000	0.000	0.010	0.0	0.00	0.000	0.725	0.740	See	See